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Record W2136433400 · doi:10.5465/ambpp.2005.18779161

HOW DO YOU CLIMB THE CORPORATE LADDER? A MULTI-REGIONAL ANALYSIS OF THE ETHICAL PREFERENCES FOR INFLUENCING SUPERIORS.

2005· article· en· W2136433400 on OpenAlex
David A. Ralston, Carolyn P. Egri, Irina Naoumova, Florian von Wangenheim, María Teresa de la Garza Carranza, Laurie P. Milton, Tânia Casado, Prem Ramburuth, Mahfooz A. Ansari, Liesl Riddle, Ilya Girson, Malika Richards, Ian Palmer, David Brock, Arif Nazir Butt, Narasimhan Srinivasan, Marina Dabić, Arūnas Starkus

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAcademy of Management Proceedings · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsWestern UniversityUniversity of CalgarySimon Fraser University
Fundersnot available
KeywordsClimbConsistency (knowledge bases)Predictive powerPower (physics)ConservatismSocial psychologyPositive economicsEconomicsPsychologyPolitical scienceComputer scienceLawEpistemology

Abstract

fetched live from OpenAlex

We investigate upward influence ethics in 35 societies. A global converging was found on the acceptability of different types of upward influence ethics. Differences among the regions, and societies within each region, as well as this overarching trend of consistency, were also found. Additionally, macrolevel (economic wealth), as well as the micro-level (egalitarian commitment-conservatism), factors provide predictive power for this model. Thus, our findings provide evidence that a global model should be based on multiple-level variables.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.115
GPT teacher head0.352
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it